In this paper, we propose a novel semi-supervised active salient object detection (SOD) method that actively acquires a small subset of the most discriminative and representative samples for labeling. Two main contributions have been made to prevent the method from being overwhelmed by labeling similar distributed samples. First, we design a saliency encoder-decoder with adversarial discriminator to generate a confidence map, representing the network uncertainty on the current prediction. Then, we select the least confident (discriminative) samples from the unlabeled pool to form the "candidate labeled pool". Second, we train a Variational Auto-Encoder (VAE) to select and add the most representative data from the "candidate labeled pool" into the labeled pool by comparing their corresponding features in the latent space. Within our framework, these two networks are optimized conditioned on the states of each other progressively. Experimental results on six benchmarking SOD datasets demonstrate that our annotation efficient learning based salient object detection method, reaching to 14% labeling budget, can be on par with the state-of-the-art fully-supervised deep SOD models. The source code is publicly available via our project page: https://github.com/JingZhang617/Semi- sup- active-selfsup-Learning . (c) 2021 Elsevier Ltd. All rights reserved.