acc refers to how often your model predicted the correct class.
loss refers to how close to the label your predictions were.
Consider a single binary classification where your prediction is 0.9 and your label is 1 (I’ll use mean absolute error as my loss function). Your prediction was closer to 1 than it was to 0, so it predicted correctly 1 out of 1 times, and your acc is 1.0 (100%). Your loss is |0.9 - 1| = 0.1, so you were close, but your model will use that loss to make its next prediction closer.