Siamese Network - Sigmoid function to compute similarity score
Your observation is correct @Stefan J, but did you observe that there is a Dense layer too? The absolute L1 distances are multiplied with the weights of the Dense layer. These outputs are then fed to the sigmoid function. If we are looking at a Keras implementation from One Shot Learning with Siamese Networks using Keras,

you see the last Dense layer.
Also, from the research paper,

The L1 distances are multiplied with $\alpha$ which is a training parameter. This sum is then passed on to the sigmoid $\sigma$ function. We may interpret these values of $\alpha$ as the weights of the last Dense layer. These weights get smaller after training.

Another obvious reason of a sigmoid function is to get similarity scores in ( 0, 1 ). The binary cross-entropy loss function is used with it.