In the context of aggregating probabilistic opinions from multiple agents facing severe uncertainty, imprecise probabilities are commonly utilized to represent their beliefs. Voting for Bins (VfB) is a novel voting method enabling agents with imprecise probabilistic beliefs to vote for sets of probability intervals, or bins. Inspired by the Condorcet Jury Theorem, VfB allows for the derivation of probabilistic assurances regarding the likelihood of identifying the correct alternative among a set, assuming the independence of the electorate and given estimates of the agents’ average competence levels. VfB also facilitates direct computation of the maximal number of bins, thereby determining the precision permitted in the voting process. In this work, we compare VfB’s performance, assessed by assigning an epistemic value to each aggregate, against standard imprecise pooling methods through multi-agent voting simulations. To the best of our knowledge, this work provides the first empirical comparison of imprecise pooling methods utilizing parameterized imprecise beliefs generated through a randomized process. Furthermore, we formally integrate VfB into the probabilistic pooling framework by examining which desirable properties, identified in the pooling literature, are satisfied by VfB.