Semantic Segmentation using Keras: loss function and mask
let me see if I can help.

(1) I would definitely recommend binary crossentropy for your loss function.

(2) Your labels should be "masks", which are images (the same size as your input images) where your "0-class" pixels are 0's and your "1-class" pixels are 1's.  This is basically a black and white image where black and white represent the 2 different classes. The output of the UNET network will be a single channel image (the same dimensions as the input) where your "0-class" is colored black and your "1-class" is colored white.

Here is an example:


The output mask in this image is what your label ("mask") should look like. 
If you train your network right, it should also be the output of UNET.  Train for about 40 epochs and you will start to see results.