How to map ground truth to prediction for UNet architecture
It depends on how your UNet architecture is constructed. 

In the example image you've shown, the output of the first few layers of convolutions is cropped to 392x392 before it's copied over to the input of the final convolutional layers. Then, it's decreased to 388x388 by convolution without padding. For the example, you're probably best off cropping your image to 392x392, then scaling down to 388x388, as the lost resolution due to the convolution is more analogous to scaling than cropping.

You could also construct the network to output an image of the same dimensionality to avoid this issue. The downside to this approach is that the network will produce some strange/poor results near the boundaries of the image, so you're spending computational power on pixels you will need to disregard anyway.