Link prediction aims at predicting future links or inferring missing links based on observed partial networks or graphs. Active link prediction has recently been shown to be highly effective at reducing the amount of data needed to train a link predictor by leveraging active learning. Existing research on active link prediction concentrated solely on the information gain caused by Shannon entropy to actively select samples. Few studies consider information gain brought by rule uncer- tainty in the process of actively selecting samples. This will cause the existing active link prediction research to be unable to use the rich semantic information contained in the knowl- edge graph rules, which is not conducive to further reducing the amount of data needed to train a link predictor. In this paper, we address the aforementioned challenge by propos- ing a Rule Guided Active Link Prediction (RGAL) approach. Through the joint modeling of information uncertainty based on Shannon entropy and information uncertainty based on rules, our proposed method can select the most informative triples while taking into account the latent semantics informa- tion contained in the triples. Extensive experiments on real- world datasets verify the effectiveness of RGAL compared with state-of-the-art methods.