In the pattern recognition community, one of the most critical problems in the design of supervised classification and regression systems is given by the quality and the quantity of the exploited training samples (ground-truth). This problem is particularly important in such applications in which the process oftraining sample collection is an expensive and time consuming task subject to different sources of errors. Active learning represents an interesting approach proposed in the literature to address the problem of ground-truth collection, in which training samples are selected in an iterative way in order to minimize the number of involved samples and the intervention of human users. This hinders the applications of supervised learning techniques to large scale problems. In this paper, we propose a high-order label correlation driven active learning (HoAL) approach that allows the iterative learning algorithm itself to select the informative example-label pairs from which it learns so as to learn an accurate classifier with less annotation efforts. Four crucial issues are considered by the proposed HoAL: unlike binary cases, the selection granularity for multilabel active learning need to be fined from example to examplelabel pair; different labels are seldom independent, and label correlations provide critical information for efficient learning; in addition to pair-wise label correlations, high-order label correlations are also informative for multilabel active learning; and since the number of label combinations increases exponentially with respect to the number of labels, an efficient mining method is required to discover informative label correlations. The proposed approach is tested on public data sets, and the empirical results demonstrate its effectiveness.