How are Accuracy and Precision related Mathematica for a given operation?

Precision is the principal representation of numerical error

Except for numbers that are equal to zero, error in arbitrary-precision numbers is stored internally as its precision. For numbers equal to zero, the accuracy is stored, because the precision turns out to be undefined (even if Precision[zero] is defined). One way to view zeros are as a form of Underflow[] for arbitrary precision numbers, which I will explain below.

For a nonzero number $x$ with precision $p$, the error bound $dx>0$ and accuracy $a$, as defined by Precision and Accuracy, are related as follows: $$ p = - \log_{10} |dx / x| \,, \quad a = - \log_{10} dx\,.\tag{1}$$ An arbitrary-precision number $x$ represents a real number $x^*$ in the interval $$ x - dx < x^* < x + dx\,.$$

For zero numbers, the precision formula $ p = - \log_{10} |dx / x|$ is undefined; however the Precision of a zero is defined to be 0. in Mathematica, which is the lowest allowed setting of $MinPrecision. Further explanation of zeros shows why there is this limit.

An arbitrary-precision zero is stored with its accuracy $a = - \log_{10} dx$. It represents a number $x^*$ in the interval $$-dx < x^* < dx \,.$$ When a computation results in $dx \ge |x|$, the result is a zero with Accuracy $a=-\log_{10}dx$. In this sense the zero represents an underflow. The limiting value $dx = |x|$ corresponds to a precision of $p = - \log_{10}|dx/x| = 0$. Defining the Precision of a zero to be 0. is consistent with this.

The function RealExponent[x] provides another way to express a connection between precision and accuracy, although in my opinion it is not the fundamental idea. When $x$ is nonzero, RealExponent[x] is simply $\log_{10} |x|$ and is related to precision and accuracy by RealExponent[x] = Precision[x] - Accuracy[x], which follows from equations (1) above. This relationship holds for zeros, too, and simplifies to RealExponent[zero] = -Accuracy[zero]. This can be taken as the definition of RealExponent[x] in terms of Precision[x] and Accuracy[x].

Error propagation and an example in the OP

It turns out that arbitrary precision numbers use a linearized model of error propagation.

(* Compute the uncertainty/error bound  d[x]  in a number  x  *)
ClearAll[d];
d[x_ /; x == 0] := 10^-Accuracy[x];
d[x_?NumericQ] := x*10^-Precision[x];

As @rhermans shows, the propagated error in multiplication is given by

(x + d[x]) (y + d[y]) - x y // Expand
(*  y d[x] + x d[y] + d[x] d[y]  *)

However, the computed Accuracy of a product more closely agrees with the linear part y d[x] + x d[y] with the quadratic term d[x] d[y] dropped. Here is a comparison of the linear and full model on the OP's example:

Block[{x = SetAccuracy[10, 3], y = SetAccuracy[1, 7]},
  {y*d[x] + x*d[y], y*d[x] + x*d[y] + d[x] d[y]}
  ];
-Log10[%] - Accuracy[Times[SetAccuracy[10, 3], SetAccuracy[1, 7]]]
(* 
  { 4.44089*10^-16,  <-- linear model off by 1 bit (ulp)
   -4.33861*10^-8}
 *)

Presumably this is done for efficiency and justified on the grounds that in a floating-point model, the term d[x] d[y] would be less than the FP epsilon and rounded off.

The composition of SetAccuracy and SetPrecision (OP's first examples)

Both SetAccuracy and SetPrecision set or reset the uncertainty/error bound of a number x, which is stored as the precision or accuracy according as x is nonzero or zero, respectively. So the first example turns out to be the accuracy of the number x = 12.3`15 with precision 15.:

Accuracy[12.3`15]
(*  13.9101  *)

Note the special case: SetPrecision[zero, prec] yields an exact 0 if zero == 0.

Unexpected result adding approximate zero

I do not understand how the point-estimate is derived for the last two and why it is less than 10^-9, 10^-8 respectively:

accEx = {  (* add a zero with d[x] == 10^-10 to various numbers *)
   0``10 + 10^-11,   (* d[x] > x *)
   0``10 + 10^-10,   (* d[x] == x *)
   0``10 + 10^-9,    (* d[x] < x *)
   0``10 + 10^-8};   (* d[x] < x *)
FullForm /@ accEx
(*
  {0``10.,
   0``10.,
   9.9999999996153512982210997961`0.9999999999832958*^-10,  Expected 1.`1.*^-9
   9.99999999999482205859102634804`2.*^-9}                  Expected 1.`2.*^-8
*)

The accuracy is preserved and the differences from the expected values are zero according to the precision, but it would also have turned so with the expected values:

Accuracy /@ accEx
(*  {10., 10., 10., 10., 10.}  *)

accEx - 10^Range[-11, -7]
(*  {0.*10^-10, 0.*10^-10, 0.*10^-10, 0.*10^-10, 0.*10^-10}  *)

Function example

The linearized model of y = f[x] is d[y] = f'[x] d[x]:

Sqrt[2.`20] // FullForm
(*  1.41421356237309504880168872420969807857`20.30102999566398  *)

The precision is 20.30102999566398, because f'[x] = 1/(2 Sqrt[x]) effectively adds Log10[2] to the precision:

Sqrt[2.`20] // Precision // FullForm
(*  20.30102999566398`  *)

-Log10[
  (1/(2 Sqrt[2.]) * 2.*10^-20) / Sqrt[2.]  (* d[y] / y *)
  ]
(*  20.30102999566398` *)

20. + Log10[2]
(*  20.30102999566398`   *)

Accuracy and Precision are related though RealExponent

The relation between Accuracy and Precision is given by

RealExponent[x] + Accuracy[x] == Precision[x]

Therefore, as RealExponent[12.3]-> 1.08991 then Accuracy[SetPrecision[12.3, 15]] must be 15 - 1.08991 -> 13.9101

Similarly, Precision[SetAccuracy[12.3, 15]] is 15 + 1.08991 -> 16.0899.

Operations

To get the Accuracy after an operation, we just need to distribute the errors.

The error of Times[a, b] is

ExpandAll[(a + δa) (b + δb) - a b]

 b δa + a δb + δa δb

where Accuracy[a] -> -Log10[δa].

Numerical example

y = SetAccuracy[10, 3];
z = SetAccuracy[1, 7];
Accuracy[y z]

2.99957 

N[-Log10[
    b δa + a δb + δa δb
    ]] /. {
  a -> y,
   b -> z,
  δa -> Power[10, -3],
  δb -> Power[10, -7]
  }

 2.99957