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Deep Neural Networks (DNNs) have been proven to be vulnerable to adversarial attacks. Existing decision-based adversarial attacks require large numbers of queries to find an effective adversarial example, resulting in a heavy query cost and also performance degradation under defenses. In this paper, we propose the Dispersed Sampling Attack (DSA), which is a query-efficient decision-based adversarial attack by exploiting the transferability of white-box perturbations. DSA can generate diverse examples with different locations in the embedding space, which provides more information about the adversarial region of substitute models and allows us to search for transferable perturbations. Specifically, DSA samples in a hypersphere centered on an original image, and progressively constrains the perturbation. Extensive experiments are conducted on public datasets to evaluate the performance of DSA in closed-set and open-set scenarios. DSA outperforms the state-of-the-art attacks in terms of both attack success rate (ASR) and average number of queries (AvgQ). Specifically, DSA achieves an ASR of about 90% with an AvgQ of 200 on 4 well-known commercial DNN services.