Loss is decreasing and accuracy is increasing but wrong predictions

I am trying to perform an image segmentation task on 3d images. As preprocessing I slice the images into 2d images.

The images do contain multiple labels. As a loss function I use the tf.keras.losses.SparseCategoricalCrossentropy and as metric I do use tf.keras.metrics.SparseCategoricalAccuracy.

When I train the model on 2 2d slices the accuracy becomes 0.99 and the loss is very low, however when I save the model and predict on the same 2 images, the prediction are completely wrong.

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Has anybody an idea what I could be missing?