Active learning is a subfield of machine learning which allows to reduce the amount of data necessary to train a classifier. The training set is built in an iterative way such that only the most significant and informative data are used and labeled by an external person called oracle. It is furthermore possible to use active learning with the theory of belief functions in order to take erroneous labels due to the oracle's uncertainty and imprecision into account in order to limit their influence on the classifier's performance. In this article, we compare the classifier of the k nearest neighbours (kNN) to a variant based on belief functions from the theory of belief functions (EkNN), in a situation where some labels have been noised in order to model uncertain labels. We show that although the superiority of EkNN over kNN is not systematic, there are some interesting and modest results supporting the relevance of belief functions in active learning.