In order to achieve better classification performance with even fewer labeled images, active learning is suitable for these situations. Several active learning methods have been proposed for multi-label image classification, but all of them assume that all training images with complete labels. However, as a matter of fact, it is very difficult to get complete labels for each example, especially when the size of labels in a multi-label domain is huge. Usually, only partial labels are available. This is one kind of "weak label" problems. This paper proposes an ingeniously solution to this "weak label" problem on multi-label active learning for image classification (called WLMAL). It explores label correlation on the weak label problem with the help of input features, and then utilizes label correlation to evaluate the informativeness of each example-label pair in a multi-label dataset for active sampling. Our experimental results on three real-world datasets show that our proposed approach WLMAL consistently outperforms existing approaches significantly.