Active learning is an established technique to reduce the labeling cost for building high-quality machine learning models. However, state-of-the-art approaches focus on maximizing the clean performance (e.g. accuracy) but disregarding robustness. In this work, we propose Robust Active Learning, an active learning process that integrates adversarial training – the most established method to produce robust models. First, we conduct an empirical study to evaluate the effectiveness of existing approaches and uncover the characteristics of data. Then, we propose a novel approach, density-based robust sampling with entropy (DRE), to target both clean performance and robustness. Our experiments are conducted on 11 acquisition functions, 4 datasets, 6 DNN architectures, and 15105 trained DNNs.