The key difference is whether inference is based on the sampling distribution or the posterior distribution.
The sampling distribution is the distribution of your statistic (MLE estimate, mean, EAP, MAP, or whatever you want) under repeated sampling from the population distribution. Frequentism is an evaluation procedure, which can be applied to any estimator whether it be Bayesian or something like MLE. Frequentists are interested in whether this distribution has "good" properties. Supposedly good properties include things like unbiasedness, consistency, minimum variance, etc. Inference is typically expressed as a function of this distribution (confidence intervals) or by comparing the sampling distribution under some restriction (the null hypothesis) to the actual value of the statistic in the observed sample.
Given that you can't typically sample from the population distribution, the practical question becomes how do you approximate the sampling distribution. Typically this is done by appealing to a central limit theorem. Bootstrapping provides another intuitive approximation.
There are all sorts of problem with this approach to statistics despite its success.