Lexical simplification (LS) aims to replace complex words in a given sentence with their simpler alternatives of equivalent meaning. Recently unsupervised lexical simplification approaches only rely on the complex word itself regardless of the given sentence to generate candidate substitutions, which will inevitably produce a large number of spurious candidates. We present a simple BERT-based LS approach that makes use of the pre-trained unsupervised deep bidirectional representations BERT. Despite being entirely unsupervised, experimental results show that our approach obtains obvious improvement than these baselines leveraging linguistic databases and parallel corpus, outperforming the state-of-the-art by more than 11 Accuracy points on three well-known benchmarks.