Probably you might be able to achieve this with something simple like this:
from torchvision.utils import save_image

# assuming your uncertainty matrix is stored in variable matrix 
save_image(1-matrix, "SPECIFY/YOUR/SAVE/PATH/uncertainty.png")

EDIT: Or if you want to plot it directly (e.g. by using matplotlib) you could do something like this:
from matplotlib import plot as plt

matrix_npy = (1-matrix).cpu().detach().numpy().squeeze()
plt.imshow(matrix_npy, cmap='gray') 
plt.show()

Next EDIT: it might be worth considering to save all 12 channels as separate uncertainty map since otherwise it could be hard to trace back a high uncertainty