We introduce the problem of PAC rank elicitation, which consists of sorting a given set of options based on adaptive sampling of stochastic pairwise preferences. More specifically, we assume the existence of a rank-ing procedure, such as Copeland's method, that deter-mines an underlying target order of the options. The goal is to predict a ranking that is sufficiently close to this target order with high probability, where closeness is measured in terms of a suitable distance measure. We instantiate this setting with combinations of two dif-ferent distance measures and ranking procedures. For these instantiations, we devise efficient strategies for sampling pairwise preferences and analyze the corre-sponding sample complexity. We also present first ex-periments to illustrate the practical performance of our methods.