Surrogate models are often used to alleviate extensive computational burden for slope reliability analysis. How to efficiently train a surrogate model with high precision is always a nontrivial task. This paper proposes a novel method, which combines the shear strength reduction (SSR) technique, the surrogate model and the adaptive pool-based sampling strategy for efficient slope reliability analysis. The surrogate model is used to replace the time-consuming slope stability model in simulation-based reliability analysis, while the SSR technique coupled with an adaptive pool-based sampling strategy is innovatively adopted to search for the most informative samples to train the surrogate model, which can improve the computational efficiency significantly. Further, an expanded sampling method is suggested to improve the local prediction of surrogate model in other design space, enabling an efficient evaluation of the slope failure probability corresponding to different threshold safety factors. The main feature of the proposed method is that it takes advantage of SSR technique to efficiently generate training samples in the region of interest. Several slope examples are analyzed to validate the accuracy, computational efficiency as well as the applicability of the proposed method. The results show that proposed method outperforms other approaches in computational efficiency and can give accurate estimation of slope failure probability as well.