Few-shot learning methods aim for good performance in the low-data regime. Structured output tasks such as segmentation present difficulties for few-shot learning because of their high dimensionality and the statistical dependencies among outputs. To tackle this problem, we propose the co-FCN, a conditional network learned by end-to-end optimization to perform fast, accurate few-shot segmentation. The network conditions on an annotated support set of images via feature fusion to perform inference on an unannotated query image. Once learned, our conditioning approach requires no further optimization for new data. Addi- tional annotated inputs are used to update the output via a single inference step, making the model suitable for interactive use. Our conditional network signifi- cantly improves few-shot accuracy over the prior state-of-the-art.