This is mostly answered by decision theory. The approach is that you must formalize what you want to achieve with your point estimate, and that can be formalized as a loss function. Examples are squared error loss:
$$
    L(\hat{\theta}, \theta) = (\hat{\theta} - \theta)^2
$$
absolute value loss, or others. Then the optimal estimator (in your bayesian setting) is given by the function $\hat{\theta}$ that minimized posterior expected loss:
$$
   E L(\hat{\theta}, \theta)
$$
where the expectation is over the posterior distribution of $\theta$. In the case of 
the squared error loss, that is the posterior expected value. 

You can read about this an any text about Bayesian decision theory, such as C Roberts: "The bayesian choice"  (Springer).