Learning from uncertain data has been drawing increasing attention in recent years. In this paper, we propose a tree induction approach which can not only handle uncertain data, but also furthermore reduce epistemic uncertainty by querying the most valuable uncertain instances within the learning procedure. We extend classical decision trees to the framework of belief functions to deal with a variety of uncertainties in the data. In particular, we use entropy intervals extracted from the evidential likelihood to query selected uncertain querying training instances when needed, in order to improve the selection of the splitting attribute. Our experiments show the good performances of proposed active belief decision trees under different conditions.