Pairwise data clustering is a well-founded grouping technique based on relational data of objects, which has a widespread application domain. However, its applicability suffers from the disadvantageous fact that N objects give rise to N(N-1)/2 relations. To cure this unfavorable scaling, techniques to sparsely sample the relations have been developed. Yet, a randomly chosen subset of the data might not grasp the structure of the complete data set. To overcome this deficit, we use active learning methods from the field of Statistical Decision Theory. Extending on existing approaches, we present a novel algorithm for actively learning hierarchical group structures based on mean field annealing optimization.