Entropy gain is widely used for learning decision trees. However, as we go deeper downward the tree, the examples become rarer and the faithfulness of entropy decreases. Thus, misleading choices and over-fitting may occur and the tree has to be adjusted by using an early-stop criterion or post pruning algorithms. However, these methods still depends on the choices previously made, which may be unsatisfactory. We propose a new cumulative entropy function based on confidence intervals on frequency estimates that together considers the entropy of the probability distribution and the uncertainty around the estimation of its parameters. This function takes advantage of the ability of a possibility distribution to upper bound a family of probabilities previously estimated from a limited set of examples and of the link between possibilistic specificity order and entropy. The proposed measure has several advantages over the classical one. It performs significant choices of split and provides a statistically relevant stopping criterion that allows the learning of trees whose size is well-suited w.r.t. the available data. On the top of that, it also provides a reasonable estimator of the performances of a decision tree. Finally, we show that it can be used for designing a simple and efficient online learning algorithm.