That's kinda true, but (regularization aside) for standard loss functions it's minimized at the point it's well calibrated, right? Given the scores in the image (97% animal, 90% tiger, etc) they seem to be binary classifiers e.g. "is this a tiger?" So of all scores in the neighborhood of 90%, 90% should be "yes it is," making it a measure of confidence compatible with probability.

Please someone correct me if I'm wrong, but I'm pretty sure that's how it works, just like how logistic regression gives you a probability.

You can't add any of the numbers in the picture to equal 1.0 (or 100)