Supervised learning from multiple labeling sources is an increasingly important problem in machine learning and data mining. This paper develops a probabilistic approach to this problem when annotators may be unreliable (labels are noisy), but also their expertise varies depending on the data they observe (annotators may have knowledge about different parts of the input space). That is, an annotator may not be consistently accurate (or inaccurate) across the task domain. The presented approach produces classification and annotator models that allow us to provide estimates of the true labels and annotator variable expertise. We provide an analysis of the proposed model under various scenarios and show experimentally that annotator expertise can indeed vary in real tasks and that the presented approach provides clear advantages over previously introduced multi-annotator methods, which only consider general annotator characteristics.