Active learning is to select the most informative examples to request their labels. Most previous studies in active learning for multi-label classification didn't pay enough attention on label correlations. This leads to a bad performance for classification. In this paper, we proposed a chi-square statistics multi-label active learning (CSMAL) algorithm, which uses chi-square statistics to accurately evaluate correlations between labels. CSMAL considers not only positive relationships but also negative ones. It uses the average correlation between a potential label and its rest unlabeled labels as the label information for each sample-label pair. CSMAL further integrates uncertainty and label information to select example-label pairs to request labels. Our empirical results demonstrate that our proposed method CSMAL outperforms the state-of-the-art active learning methods for multi-label classification. It significantly reduces the labeling workloads and improves the performance of a classifier built.