Multi-modal learning has shown advantages in improving weakly-supervised medical image segmentation (WS- MIS). However, most current works are based on paired data, which is infeasible to collect in certain scenarios. Although modal translation can be used to generate paired data, it often leads to low-quality translations, such as local deformations or irrational textures, without prior knowledge. This paper proposes a discriminative-aware image translation method, which introduces class activation maps (CAMs) to localize discriminative areas, thus overcoming the lack of pixel-wise annotations in WS-MIS. In addition, we design a CAM-correlation constraint that facilitates multi-modal complementary information exchange to enhance the consistency between CAMs generated from different modalities. Experimental results show that our method outperforms recent weakly-supervised segmentation works when using unpaired multi-modal data.