Nowadays, crowdsourcing has been commonly used to enlist label information both effectively and efficiently. One major challenge in crowdsourcing is the diverse worker quality, which determines the accuracy of the label information provided by such workers. Motivated by the observation that in many crowdsourcing platforms, the same set of workers typically work on the same set of tasks, we propose to model the diverse worker quality by studying their behaviors across multiple related tasks. To this end, we propose an optimization framework named MultiC2 for learning from task and worker dual heterogeneity. It uses a weight tensor to represent the workers' behaviors across multiple tasks, and seeks to find the optimal solution of the tensor by exploiting its structured information. We then propose an iterative algorithm to solve the optimization framework and analyze its computational complexity. To infer the true label of an example, we construct a worker ensemble based on the estimated tensor, whose decisions will be weighted using a set of entropy weight. Finally, we test the performance of MultiC2 on various data sets, and demonstrate its superiority over state-of-the-art crowdsourcing techniques.