The recently emerged in-context-learning-based (ICL-based) models have the potential towards the unification of medical lesion segmentation. However, due to their cross-fusion designs, existing ICL-based unified segmentation models fail to accurately localize lesions with low-matched reference sets. Considering that the query itself can be regarded as a high-matched reference, which better indicates the target, we design a self-referencing mechanism that adaptively extracts self-referring indicator vectors from the query based on coarse predictions, thus effectively overcoming the negative impact caused by low-match reference sets. To further facilitate the self-referring mechanism, we introduce reference indicator generation to efficiently extract reference information for coarse predictions instead of using cross-fusion modules, which heavily rely on reference sets. Our designs successfully address the challenges of applying ICL to unified medical lesion segmentation, forming a novel framework named SR-ICL. Our method achieves state-of-the-art results on 8 medical lesion segmentation tasks with only 4 image-mask pairs as reference. Notably, SR-ICL still accomplishes remarkable performance even when using weak reference annotations such as boxes and points, and maintains fixed and low memory consumption even if more tasks are combined. We hope that SR-ICL can provide new insights for the clinical application of medical lesion segmentation.