Label correlation analysis is very important for multi-label classification. And there is no study to measure the label correlation for example-label based active learning. In this paper, from a statistical point of view, we proposed a cosine similarity based multi-label active learning (CosMAL), which uses cosine similarity to accurately evaluate the correlations between all labels. It further uses the average correlation between the potential label and the other unlabeled labels as the label information for each sample-label pair. And then we select the most informativeness example-label pairs. Our empirical results demonstrate that our proposed method CosMAL outperforms the state-of-the-art active learning for multi-label classification. It significantly reduces the labeling workload and improves the performance of a classifier learned.