We present an active learning method for coreference resolution that is novel in three respects. (i) It uses bootstrapped neighborhood pooling, which ensures a class-balanced pool even though gold labels are not available. (ii) It employs neighborhood selection, a selection strategy that ensures coverage of both positive and negative links for selected markables. (iii) It is based on a query-by-committee selection strategy in contrast to earlier uncertainty sampling work. Experiments show that this new method outperforms random sampling in terms of both annotation effort and peak performance.