Automatic aortic image segmentation plays an important role in early aortic disease diagnosis, risk evaluation and treatment planning. In this paper, we use a multi-atlas based medical image segmentation method and first combine it with a joint label fusion(JLF) strategy to segment 3D aortic CT images automatically. Joint label fusion strategy takes the correlation of atlases into consideration and the effect of redundant information of atlases can be restrained. To handle the problem of insufficient atlases, we propose an atlas archive update method which can enhance the segmentation accuracy with relatively low computational complexity. We evaluate our method by using a data set with 15 aortic subjects and comparing with three widely used label fusion techniques (majority voting, local-weighted label fusion and STAPLE). Experimental results show superior performances of our method to state-of-the-art.