Expected empirical entropy
Here is another approach, based on information theory and heavily inspired by @usul's answer. It shows that $\epsilon_n=O(1)$ with very few calculations, and can be used to prove that $\epsilon_n \rightarrow \log_2 \sqrt{e}$ and to derive good estimates on the rate of convergence with less calculations than @usul's approach. In fact, I find a closed-form expression for $\epsilon_n$ : $$(1)  \; \; \; \; \epsilon_n = n \left( H(Binom(n,1/2)) - H \left( Binom \left( n-1, 1/2 \right) \right) \right) \ .$$

Details:

Let $X$ be a uniform random variable in $\{0,1\}^n$. Let $K$ be a random variable equal to the number of 1's in $X$. The expression $\#$ that @Danny wants to analyze is exactly equal to $n \cdot \mathbb{E}_{k} [H(X_1 | K=k)]$. (Here $X_1$ is the first bit of $X$.) By the basic properties of the entropy operator,
$$(2)  \; \; \; \; \# = n\mathbb{E}_{k} [H(X_1 | K=k)]=nH(X_1|K)=n(H(X_1K)-H(K))=n(H(K|X_1)+H(X_1)-H(K)) = n(1-H(Binom(n,1/2)) + H(Binom(n-1,1/2))) \ .$$
The last equality follows from the fact that $X_1$ is just a uniformly random bit, $K$ is the binomial distribution, and $K|(X_1=x_1)$ is distributed either as $Binom(n-1,1/2)$ or as $Binom(n-1,1/2)+1$, depending on the value of $x_1$, both of which have the same entropy.

This already gave us equation (1). Now we just need to calculate to get the value of $\lim_{n \rightarrow \infty} \epsilon_n$

We use any known estimation for the entropy of a binomial RV, such as here. We see that $$ (3)  \; \; \; \; H(K)=H(Binom(n,1/2))=\frac12 \log_2 ( \pi en / 2) + O(1/n) \ ,$$ and, similarly, that $$H(K|X_1)=H(Binom(n-1,1/2))=\frac12 \log_2 ( \pi e(n-1)/2) + O(1/n) \ .$$ Canceling out terms and substituting into (1), we get $$ (4)  \; \; \; \; \epsilon_n = n \cdot (H(K)-H(K|X_1)) = n \cdot \frac12 (\log_2 (n/(n-1)) + O(1/n)) = \\ \frac12 \log_2 ((n/(n-1))^n) + O(1) \rightarrow \log_2(\sqrt{e}) + O(1) \ . $$

By slightly improving the approximation (3) we should be able to replace the $O(1)$ term in (4) by $O(1/n)$ and therefore get that, indeed, $\lim_{n \rightarrow \infty} \epsilon_n = \log_2 \sqrt{e}$. To get this better estimation it should be enough to check that $H(Binom(n,1/2))=\frac12 \log_2 ( \pi en / 2) + err(n)$ where $err(n)=O(1/n)$ and $err$ is a monotone function.