Title: Active Learning with Partial Labels. Abstract: In this paper, we for the first time study a new problem setting called active learning with partial labels (ALPL), where an oracle provides the query samples with a set of candidate labels that contains the true label. Such a setting relaxes the oracle from the demanding labeling process. To address ALPL, we firstly propose a firm and intuitive baseline by directly adapting a state-of-the-art method for learning with partial labels to train the predictor, which can be seamlessly incorporated into existing AL frameworks. Inspired by human inference in cognitive science, we propose to improve the baseline by exploiting and exploring counter examples (CEs) to relieve the overfitting caused by a few training samples in ALPL. Specifically, we propose to construct CEs by reversing the partial labels for each instance, learning from which we propose a simple but effective WorseNet. By leveraging the distribution gap between WorseNet and the predictor, both the predictor itself and the sample selection process can be improved. Experimental results on five real-world datasets and four benchmark datasets show that our proposed methods achieve comprehensive improvements over ten representative AL frameworks, highlighting the superiority and effectiveness of CEs and WorseNet.  