I will first consider the case of a $4\times 4$ matrix $Q$, and generalize to a higher dimensional $Q$ at the end.

**A. The four-by-four case.**

It is helpful to start from the polar decomposition

$$Q=
\left(\begin{array}{cc}U'&0\\ 0&V'\end{array}\right)
\left(\begin{array}{cc}\sqrt{1-T}&\sqrt{T}\\
\sqrt{T}&-\sqrt{1-T}
\end{array}\right)
\left(\begin{array}{cc}U&0\\ 0&V\end{array}\right)
$$
where $U,V,U',V'$ are four unitary matrices and
$$
T=\left(\begin{array}{cc}T_1&0\\ 0&T_2\end{array}\right)$$
is a diagonal matrix with diagonal elements $0\leq T_n\leq 1$.

You seek the expectation value of the matrix 
$$M=[I-U^{\rm H}(1-T)U]^{-1}=U^{\rm H}T^{-1}U$$
(I have used that $U^{\rm H}U=I$.)

The Haar distribution for $Q$ implies a Haar distribution for $U$, and moreover implies for $T_1,T_2$ the following distribution [*]

$$P(T_1,T_2)=6(T_1-T_2)^2,\;\;0\leq T_n\leq 1$$

The marginal distribution for $T_1$ is

$$P(T_1)=2-6T_1+6T_1^2$$

So you see that the expectation value of $M$ diverges: $E(M)=E(1/T_1)\,I=\infty$.

**B. The higher-dimensional case.** 

Now let's consider an $N\times N$ dimensional unitary matrix $Q$, with $N\geq 4$. The $2\times 2$ upper-left block is still of the form $U'TU$, with unitary $U'$, $U$, so we still have $M=U^{\rm H}T^{-1}U$. The Haar distribution of $Q$ still implies a Haar distribution for $U$, what changes is the distribution of $T_1$ and $T_2$ [*]

$$P(T_1,T_2)=C(T_1-T_2)^2 (T_1 T_2)^{N-4}$$

with normalization constant $C=\frac{1}{2}(N-2)^2(N^2-4N+3)$. 
The marginal distribution of $T_1$ is

$$P(T_1)=CT_1^{N-4}\left(\frac{1}{N-1}-\frac{2T_1}{N-2}+\frac{T_1^2}{N-3}\right)$$

Now the expectation of $M$ converges,

$$E(M)=\frac{N-2}{N-4}I$$

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[*] See Equation (2.10) of this <A HREF="http://arxiv.org/abs/cond-mat/9612179">review.</A>