We study the problem of selecting K arms with the highest expected rewards in a stochastic n-armed bandit game. Instead of using existing evaluation metrics (e.g., misidentification probability (Bubeck et al., 2013) or the metric in EXPLORE-K (Kalyanakrishnan & Stone, 2010)), we propose to use the aggregate regret, which is defined as the gap between the average reward of the optimal solution and that of our solution. Besides being a natural metric by itself, we argue that in many applications, such as our motivating example from crowdsourcing, the aggregate regret bound is more suitable. We propose a new PAC algorithm, which, with probability at least 1 - δ, identifies a set of K arms with regret at most ε. We provide the sample complexity bound of our algorithm. To complement, we establish the lower bound and show that the sample complexity of our algorithm matches the lower bound. Finally, we report experimental results on both synthetic and real data sets, which demonstrates the superior performance of the proposed algorithm.